Estimation and Analysis of the Electric Arc Furnace Model Coefficients

Dietz, Markus; Grabowski, Dariusz; Klimas, Maciej; Starkloff, Hans-Jörg

doi:10.1109/tpwrd.2022.3163815

Cited by 19 publications

(9 citation statements)

References 28 publications

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“…Manchur [13] Alkaran [14] Marulanda [15] Esfahani [16] Golestani [17] Illahi [18] Samet [19] Deepak [20] Dietz [21] Non-linear…”

Section: A) Linearity Versus Non-linearitymentioning

confidence: 99%

“…The model that is being discussed in [20] is a hybrid of two previous EAF models that used transition functions, the exponential and hyperbolic models. A method for estimating EAF model coefficients using measurement data, such as voltage and current waveforms captured during an EAF work cycle's melting stage, is presented in [21]. Iteratively applying the Monte Carlo approach and evolutionary algorithm to each of the input signal's designated frames is how the estimating procedure is carried out.…”

Section: A) Linearity Versus Non-linearitymentioning

confidence: 99%

“…So, to be able to use the model in the flicker studying, it is necessary to consider a time-varying EAF model. Some of the time-varying models are proposed in [16][17][18][19]21]. In [17,19], ARMA models are used for this purpose.…”

Section: B) Dynamic Versus Staticmentioning

confidence: 99%

“…In fact, in a dynamic model, the electrical quantities in the previous moments are used. Some of the dynamic models are proposed in [14,15,17,[19][20][21]. c) Time-constant versus time-varying parameters Time-varying parameters cause flicker.…”

Section: B) Dynamic Versus Staticmentioning

confidence: 99%

See 3 more Smart Citations

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Babaei,

Samet

2024

IET Generation Trans & Dist

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An accurate model of electric arc furnaces (EAFs) is required to analyse their impact on the system's power quality. One of the well‐known approaches to model EAF is using the power balance equation. This model has two terms, each one has a coefficient named the model parameter. Also, both terms are powered to values, which are called model orders. So, the model includes two model parameters and two model orders. Model orders highly affect the modelling precision. The optimum model orders have not been obtained yet in previous studies. Here, based on many actual records from EAFs, the best model orders for refining, melting, and scrapping stages are attained. Unlike the original power balance equation which has constant model parameters, the first proposed model uses the attained optimal model orders with the time‐varying model parameters. In the second proposed model, the model orders are also considered time‐varying. Model parameters, which are updated every 10 ms, are obtained using autoregressive moving average (ARMA) models. The correctness of the models is assessed by calculating some important electric quantities using the actual data and the proposed models. Finally, the time‐varying parameters are generated using two MATLAB codes.

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“…Manchur [13] Alkaran [14] Marulanda [15] Esfahani [16] Golestani [17] Illahi [18] Samet [19] Deepak [20] Dietz [21] Non-linear…”

Section: A) Linearity Versus Non-linearitymentioning

confidence: 99%

Section: A) Linearity Versus Non-linearitymentioning

confidence: 99%

Section: B) Dynamic Versus Staticmentioning

confidence: 99%

Section: B) Dynamic Versus Staticmentioning

confidence: 99%

See 2 more Smart Citations

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Babaei,

Samet

2024

IET Generation Trans & Dist

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“…In recent years, increasing attention has been paid to analyzing and determining the parameters inherent in the power balance equation. Various methodologies have been explored, including analytical solutions [35,36], as well as optimization techniques such as Monte Carlo [37], genetic algorithms [37][38][39] and particle swarm optimization (PSO) [38]. In addition, efforts have been made to understand the stochastic properties of these parameters using approaches such as the ARIMA model [37] and the LSTM neural network [39].…”

Section: Introductionmentioning

confidence: 99%

Arc Quality Index Based on Three-Phase Cassie–Mayr Electric Arc Model of Electric Arc Furnace

Blažič,

Škrjanc,

Logar

2024

Metals

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In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

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Application of Artificial Neural Networks in Electric Arc Furnace Modeling

Klimas,

Grabowski

2023

Lecture Notes in Computer Science

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Estimation and Analysis of the Electric Arc Furnace Model Coefficients

Cited by 19 publications

References 28 publications

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Arc Quality Index Based on Three-Phase Cassie–Mayr Electric Arc Model of Electric Arc Furnace

Application of Artificial Neural Networks in Electric Arc Furnace Modeling

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